ArticlePeerJ2017
HRV-derived data similarity and distribution index based on ensemble neural network for measuring depth of anaesthesia.
Article in PeerJ, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Utility of the Perfusion Index as an Indicator of Anesthetic Depth during Total Intravenous Anesthesia in Neurosurgery with Intraoperative Neuromonitoring : A Prospective Observational Study.Journal of Korean Neurosurgical Society · 2026Article
- The ANI monitor's "Energy" variable detects autonomic state modification during general anesthesia, sedation and spinal anesthesia: a retrospective cohort study.Journal of clinical monitoring and computing · 2026Article
- Optimizing the timing of painless gastroscope insertion: integrating advanced monitoring tools to enhance safety and efficacy: a narrative review.Frontiers in medicine · 2026Review
- A Machine Learning Approach for Real-Time Detection of Inadequate Sedation Using Non-EEG Physiological Signals.Bioengineering (Basel, Switzerland) · 2025Article
- Heart rate variability as a predictor of intraoperative autonomic nervous system homeostasis.Journal of clinical monitoring and computing · 2024Article
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- Heart rate variability in anaesthesiology - narrative review.Anaesthesiology intensive therapy · 2023Review
- Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder.Sensors (Basel, Switzerland) · 2021Article
- Blockade of fructose transporter protein GLUT5 inhibits proliferation of colon cancer cells: proof of concept for a new class of anti-tumor therapeutics.Pharmacological reports : PR · 2021Article
- Bispectral Index Alterations and Associations With Autonomic Changes During Hypnosis in Trauma Center Researchers: Formative Evaluation Study.JMIR formative research · 2021Article
- Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states.BMC anesthesiology · 2021Article
- Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia withSensors (Basel, Switzerland) · 2019Article
- Article
- Frontal EEG Temporal and Spectral Dynamics Similarity Analysis between Propofol and Desflurane Induced Anesthesia Using Hilbert-Huang Transform.BioMed research international · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evaluation of depth of anaesthesia (DoA) is critical in clinical surgery. Indices derived from electroencephalogram (EEG) are currently widely used to quantify DoA. However, there are known to be inaccurate under certain conditions; therefore, experienced anaesthesiologists rely on the monitoring of vital signs such as body temperature, pulse rate, respiration rate, and blood pressure to control the procedure. Because of the lack of an ideal approach for quantifying level of consciousness, studies have been conducted to develop improved methods of measuring DoA. In this study, a short-term index known as the similarity and distribution index (SDI) is proposed. The SDI is generated using heart rate variability (HRV) in the time domain and is based on observations of data distribution differences between two consecutive 32 s HRV data segments. A comparison between SDI results and expert assessments of consciousness level revealed that the SDI has strong correlation with anaesthetic depth. To optimise the effect, artificial neural network (ANN) models were constructed to fit the SDI, and ANN blind cross-validation was conducted to overcome random errors and overfitting problems. An ensemble ANN was then employed and was discovered to provide favourable DoA assessment in comparison with commonly used Bispectral Index. This study demonstrated the effectiveness of this method of DoA assessment, and the results imply that it is feasible and meaningful to use the SDI to measure DoA with the additional use of other measurement methods, if appropriate.
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